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Journal Article
Cheng, Xiuzhen, Chellappan, Sriram, Cheng, Wei, Sahin, Gokhan.  2020.  Guest Editorial Introduction to the Special Section on Network Science for High-Confidence Cyber-Physical Systems. IEEE Transactions on Network Science and Engineering. 7:764–765.
The papers in this special section focus on network science for high confidence cyber-physical systems (CPS) Here CPS refers to the engineered systems that can seamlessly integrate the physical world with the cyber world via advanced computation and communication capabilities. To enable high-confidence CPS for achieving better benefits as well as supporting emerging applications, network science-based theories and methodologies are needed to cope with the ever-growing complexity of smart CPS, to predict the system behaviors, and to model the deep inter-dependencies among CPS and the natural world. The major objective of this special section is to exploit various network science techniques such as modeling, analysis, mining, visualization, and optimization to advance the science of supporting high-confidence CPS for greater assurances of security, safety, scalability, efficiency, and reliability. These papers bring a timely and important research topic. The challenges and opportunities of applying network science approaches to high-confidence CPS are profound and far-reaching.
Conference Name: IEEE Transactions on Network Science and Engineering
Conference Paper
Yu, Wenchao, Zheng, Cheng, Cheng, Wei, Aggarwal, Charu C., Song, Dongjin, Zong, Bo, Chen, Haifeng, Wang, Wei.  2018.  Learning Deep Network Representations with Adversarially Regularized Autoencoders. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2663-2671.

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.

Yu, Wenchao, Cheng, Wei, Aggarwal, Charu C., Zhang, Kai, Chen, Haifeng, Wang, Wei.  2018.  NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2672-2681.

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors'' deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NetWalk has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3) flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NetWalk.

Sun, Bin, Cheng, Wei, Goswami, Prashant, Bai, Guohua.  2017.  An Overview of Parameter and Data Strategies for k-Nearest Neighbours Based Short-Term Traffic Prediction. Proceedings of the 2017 International Conference on E-Society, E-Education and E-Technology. :68–74.
Modern intelligent transportation systems (ITS) requires reliable and accurate short-term traffic prediction. One widely used method to predict traffic is k-nearest neighbours (kNN). Though many studies have tried to improve kNN with parameter strategies and data strategies, there is no comprehensive analysis of those strategies. This paper aims to analyse kNN strategies and guide future work to select the right strategy to improve prediction accuracy. Firstly, we examine the relations among three kNN parameters, which are: number of nearest neighbours (k), search step length (d) and window size (v). We also analysed predict step ahead (m) which is not a parameter but a user requirement and configuration. The analyses indicate that the relations among parameters are compound especially when traffic flow states are considered. The results show that strategy of using v leads to outstanding accuracy improvement. Later, we compare different data strategies such as flow-aware and time-aware ones together with ensemble strategies. The experiments show that the flow-aware strategy performs better than the time-aware one. Thus, we suggest considering all parameter strategies simultaneously as ensemble strategies especially by including v in flow-aware strategies.
Cheng, Wei, Zhang, Kai, Chen, Haifeng, Jiang, Guofei, Chen, Zhengzhang, Wang, Wei.  2016.  Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :805–814.

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.

Cheng, Wei, Zhang, Kai, Chen, Haifeng, Jiang, Guofei, Chen, Zhengzhang, Wang, Wei.  2016.  Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :805–814.

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.

Huang, Baohua, Jia, Fengwei, Yu, Jiguo, Cheng, Wei.  2016.  A Transparent Framework Based on Accessing Bridge and Mobile App for Protecting Database Privacy with PKI. Proceedings of the 1st ACM Workshop on Privacy-Aware Mobile Computing. :43–50.

With the popularity of cloud computing, database outsourcing has been adopted by many companies. However, database owners may not 100% trust their database service providers. As a result, database privacy becomes a key issue for protecting data from the database service providers. Many researches have been conducted to address this issue, but few of them considered the simultaneous transparent support of existing DBMSs (Database Management Systems), applications and RADTs (Rapid Application Development Tools). A transparent framework based on accessing bridge and mobile app for protecting database privacy with PKI (Public Key Infrastructure) is, therefore, proposed to fill the blank. The framework uses PKI as its security base and encrypts sensitive data with data owners' public keys to protect data privacy. Mobile app is used to control private key and decrypt data, so that accessing sensitive data is completely controlled by data owners in a secure and independent channel. Accessing bridge utilizes database accessing middleware standard to transparently support existing DBMSs, applications and RADTs. This paper presents the framework, analyzes its transparency and security, and evaluates its performance via experiments.